System and method to identify data flows and data mappings across systems

ABSTRACT

Aspects of the subject disclosure may include, for example, a processing system performing a method of receiving a plurality of data change transactions, wherein each data change transaction of the plurality of data change transactions comprises data object values, identifying by the processing system, data change groups indicating correlated data flows between databases by comparing data object values of the plurality of data change transactions, and determining data mappings of data objects stored in databases by statistical analysis of the correlated data flows. Other embodiments are disclosed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/223,708 filed on Apr. 6, 2021. All sections of the aforementionedapplication are incorporated by reference herein in their entirety.

FIELD OF THE DISCLOSURE

The subject disclosure relates to a system and method to identify dataflows and data mappings across systems.

BACKGROUND

Determining system-to-system data flows involves manual effort analyzingdocumentation that may or may not exist anymore or may be inaccurate dueto a lack of maintenance and/or reverse engineering existing datamanipulation implementation code.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system functioning within a communication network inaccordance with various aspects described herein.

FIG. 2B is a block and flow diagram illustrating an example,non-limiting embodiment of an implementation of analysis system inaccordance with various aspects described herein.

FIGS. 2C and 2D are block diagrams illustrating an example, non-limitingembodiment of data change transactions provided to analysis system inaccordance with various aspects described herein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for analyzing data objects and data flows between systemswithout examining the database manipulation language to ascertain theconfiguration and maintenance of the persistent storage of information.Other embodiments are described in the subject disclosure.

One or more aspects of the subject disclosure include a device having aprocessing system including a processor; and a memory that storesexecutable instructions that, when executed by the processing system,facilitate performance of operations, including receiving a plurality ofdata change transactions, wherein each data change transaction of theplurality of data change transactions comprises a timestamp, adescription of each data change transaction performed on a database anddata object values; identifying data change groups indicating correlateddata flows between databases by comparing data object values of theplurality of data change transactions; and determining data mappings ofdata objects stored in databases by statistical analysis of thecorrelated data flows.

One or more aspects of the subject disclosure include a machine-readablemedium, comprising executable instructions that, when executed by aprocessing system including a processor, facilitate performance ofoperations including receiving a plurality of data change transactions,wherein each data change transaction of the plurality of data changetransactions comprises a timestamp, a description of each data changetransaction performed on a database and data object values; identifyingdata change groups indicating correlated data flows between databases bycomparing data object values of the plurality of data changetransactions; and determining data mappings of data objects stored indatabases by statistical analysis of the correlated data flows.

One or more aspects of the subject disclosure include a processingsystem performing a method of receiving a plurality of data changetransactions, wherein each data change transaction of the plurality ofdata change transactions comprises data object values, identifying bythe processing system, data change groups indicating correlated dataflows between databases by comparing data object values of the pluralityof data change transactions, and determining data mappings of dataobjects stored in databases by statistical analysis of the correlateddata flows.

Referring now to FIG. 1 , a block diagram is shown illustrating anexample, non-limiting embodiment of a system 100 in accordance withvarious aspects described herein. For example, system 100 can facilitatein whole or in part gathering and sending copies of databasetransactions, identifying data change groups indicating correlated dataflows between databases by comparing data object values of the databasetransactions and determining data mappings of data objects stored indatabases by statistical analysis of the correlated data flows. Inparticular, a communications network 125 is presented for providingbroadband access 110 to a plurality of data terminals 114 via accessterminal 112, wireless access 120 to a plurality of mobile devices 124and vehicle 126 via base station or access point 122, voice access 130to a plurality of telephony devices 134, via switching device 132 and/ormedia access 140 to a plurality of audio/video display devices 144 viamedia terminal 142. In addition, communication network 125 is coupled toone or more content sources 175 of audio, video, graphics, text and/orother media. While broadband access 110, wireless access 120, voiceaccess 130 and media access 140 are shown separately, one or more ofthese forms of access can be combined to provide multiple accessservices to a single client device (e.g., mobile devices 124 can receivemedia content via media terminal 142, data terminal 114 can be providedvoice access via switching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system functioning within a communication network inaccordance with various aspects described herein. As shown in FIG. 2A,system 200 comprises an analysis system 210, an enterprise data storagesystem 220, a communications network 225 and a message queue 230.Enterprise data storage system 220 comprises one or more databases 221comprising plural relational tables 222 or other data objects, includingbut not limited to non-relational databases such as document stores,columnar stores and other persistent data storage. Enterprise datastorage system 220 may also comprise a database server 223.Additionally, enterprise data storage system 220 may comprise a virtualdatabase server 224. In an embodiment, enterprise data storage system220 may also comprise a message bus interface or application programinterface (API) invocations. Further, enterprise data storage system 220comprises a data manipulation language (DML) 226. DML 226 is illustratedas a single, enterprise-wide entity; however, DML 226 may be distributedthroughout enterprise data storage system 220.

Enterprise data storage system 220 may comprise hundreds of differingimplementations of databases and persistent data storage devices, manyof which that may be in use for extended periods of time (several yearsor even decades). Such legacy systems may be built on rather oldmachines that were implemented by personnel no longer associated withthe enterprise but maintained by current personnel. The conceptualdatabase is merely an abstraction of the real world as it pertains tothe users of the database. Database management systems (DBMS) comprise adata definition language (DDL) that describes the conceptual schema ofthe databases and an implementation of the conceptual schema by aphysical scheme, or even a schema-less DBMS. Throughout the years, manyDBMS have been migrated to newer solutions and/or patched into variousother DBMS to create the overall physical implementation of theenterprise data storage system 220. This bringing together of files andother information storage is an evolving process that seeks to maintainthe integrity of the data while continuing to serve the data processingneeds of the enterprise. As such systems grow larger, the volumes ofdata kept, and complexity of the interrelationships can grow enormous.

Furthermore, DBMS comprise a data manipulation language (DML) thatenables users to query the database and express commands that manipulatethe data stored within the database. The DML 226 is used to implementdata changing transactions including add/insert, modify/update andremove/delete. Such operations can be performed on tuples within therelational table 222 of the database 221, hereinafter referred to as“objects,” including, but not limited to, a document store or othernon-relational DBMS implementation that may result in side effects thatcan be traced and analyzed. A wide variety of DML may exist in anenterprise data storage system 220, depending upon the number of systemsand implementation choices made by the designers. Thus, the software or“code” for DML 226 may be quite modern or alternatively appear asunintelligible ancient hieroglyphics to modern database managers.

Typically, an analysis and documentation of system-to-system data flowsinvolves manual effort analyzing documentation that may or may not existany longer or may be inaccurate due to a lack of maintenance, and/orreverse engineering existing DML implementation code. Such tasks can beexpensive and time intensive. An analysis of one system-to-systeminterface or data mapping can take weeks to months and becomesinaccurate with the next project that impacts that system-to-systeminterface.

Analysis system 210 provides an automated analysis and documentation ofsystem-to-system correlated data flows and data mappings, includingmetadata (object/attribute, table/column, document/element) informationabout data elements persistently stored within enterprise data storagesystem 220. Analysis system 210 derives this information based on timecorrelated data changing transactions (add/insert, modify/update,remove/delete) on data content stored within enterprise data storagesystem 220, e.g., from transaction logs of the databases in enterprisedata storage system 220.

Specifically, data change transactions 227, 228, 229 (add/insert,modify/update, remove/delete) can be identified per DBMS in enterprisedata storage system 220, along with the before and after change datacontent, metadata and a timestamp when the change transaction occurred.This identification is also known as CDC or Change Data Capture. In anembodiment, a data change event capture system interfaces with each DBMSto fork a copy of the data change transactions 227, 228, 229 and tocreate a message along with a timestamp for submission to message queue230. In an embodiment, the data change transactions are submitted tomessage queue 230 in a consistent format, preferably a Java ScriptObject Notation (JSON) format, but any defined data structure consumableby the analysis system 210 may be used. In an embodiment, the datachange event capture system comprises Oracle Golden Gate, but any CDCthat enables the capture of events for packaging into the defined datastructure for 210 and sending that information to 210 will work. Pushingthose data change transactions 227, 228, 229 to analysis system 210enables a time correlated data content comparison. In an embodiment,similar data change transactions can be generated from secondaryconsumers of a message bus interface, or from API invocations (e.g.,using a pass-through API façade or behind the scenes duplication of anAPI invocation) and a copy can be passed through message queue 230 intoanalysis system 210 as well. In an embodiment, analysis system 210statistically analyzes the transactions occurring in enterprise datastorage system 220 to ascertain the schema of the databases kept withinthe system, as well as the interrelationship between data elementsbrought about by system-to-system data flow transactions generated byDML 226.

FIG. 2B is a block and flow diagram illustrating an example,non-limiting embodiment of an implementation of analysis system inaccordance with various aspects described herein. As shown in FIG. 2B,analysis system comprises a first database 211 comprising a sliding timewindow of data change transactions, a second database 212 comprisingmapping probability data, a third database 213 comprising data mappingsand data objects, a fourth database 214 comprising cross-system dataflows, and a fifth database 215 comprising system behavior andfunctionality data.

In step 231, data change transactions are received from message queue230 and stored in first database 211. First database 211 comprises datachange transactions kept within a sliding time window that enables anidentification that a data value was passed from one system to another.For example, an initial entry of ‘Postal Address 123’ into a Salessystem results in an appearance of that ‘Postal Address 123’ in anOrdering system at some later point in time, but not too far in thefuture, and preferably within the sliding time window. The migration ofthe information depends among activities of the business process flowand systems typical duration to perform their functions. In step 232,the data change transactions are asynchronously removed from the firstdatabase. The data change transactions are kept in the first database211 for the typical business cycle so that they can be used to enablecross-system content comparison.

Next in step 233, the analysis system 210 adds per-system metadata foridentified data values, to provide a candidate group of system specificelements acting in the data flow and data mapping, specifically on theappearance of data change transactions in systems that are not theoriginal source of data entry. The candidate group is a list of highprobability elements that were identified statistically by the ongoinganalysis of actual data flows as potential mapping target. For example,if an address line ‘Postal Address 123’ from system A would flow to asystem B, and the analysis system 210 identifies that ‘Postal Address123’ is typically stored in a field called ‘Addr1’ in system B. If amapping of a value ‘500’ from system A statistically flows into a fieldcalled ‘Speed’ in system B, but also into a field called ‘Bandwidth’ insystem C, the candidate group would be both fields ‘Speed’ and‘Bandwidth,’ each with the respective metadata about system,table/object/entity, etc. The purpose of the candidate group is toincrease positive mapping tests at runtime, by prioritizing knownpositive matches from past mapping over any other field existing in atarget system of a flow. The candidate group works on the metadata,i.e., system field/element names. It adds this metadata as refinement oflearned statistics to the value mapping, specifically when the datavalue (content) is not very specific and may be valid for differentmeanings. For example, a value ‘500’ from system A could be a housenumber, a speed value, a weight, length, payment amount, or other item.Adding system A metadata, e.g., ‘Speed,’ will enable the lookup of whichtarget system fields typically were used to store ‘Speed’ from system A.This list of typical (match probability based on statistics gatheredthrough previous mappings) fields is the candidate group. The candidategroup can be used to improve typical match statistics gathering byprioritizing likely matches over unlikely matches. The candidate groupcan further be used to, e.g., direct unlikely or highly unspecificmatches to longer-running process steps. This is performed using across-system data content comparison.

In step 234, the analysis system 210 identifies such candidate groupsand in step 235 stores them as a mapping probability in second database212. The analysis system 210 maps metadata to data change transactionsusing the mapping probability, to improve efficiency for anticipatedpositive mapping candidate groups. The analysis system 210 may enablelonger-running analyses to determine previously unknown mappings. Datafields of the candidate groups typically change in close time proximityin a system. This enables the analysis system 210 to conduct probabilityassessments when the data content is unspecific or can have differentpossible meanings.

Initially, the analysis system 210 will have to mass-compare many dataelements of systems where data content values are relatively unspecific,which can be improved by data element and metadata elementprioritization using machine learning (ML) provided statistics from theflow of data from system to system. Over time, using ML statisticalanalysis will sharpen that candidate group and identify the mostprobable, and eventually, the actual element name receiving a datacontent from another system, which in turn improves the efficiency ofthe analysis system 210. Analysis system 210 can update the statisticalanalysis after receiving additional data change transactions. In step236, the analysis system 210 will thus identify data objects and groupsof typical, jointly changing data fields and stores such data mappingsand data objects in third database 213. The identification of these datachange groups, i.e., the group of data fields typically jointlychanging, can use a close time proximity analysis. For example, if asystem enters a postal address, the address is typically jointly enteredwith a city, postal code, country or state codes, etc., which may appearas single transaction to a table or object. Thus, the change timestampof those fields may be equal or in close time proximity, which allows toidentify them as ‘changing together’ and therefore building a datachange group. With statistics, the data change groups can become a dataobject, i.e., statistics around these data change groups sharpen thedefinition of actual objects (sometimes only a subset of fields isentered, sometimes only some values are changed, but over time fieldsthat typically change in close time proximity identify an actual dataobject, or entity). Such data objects may also reflect complexstructures, like multiple tables in a relational system used to model anobject, or nested substructures in document stores.

Analysis system 210 stores information of cross-system data flows infourth database 214. Data change transaction timestamps enablecross-system sequencing at several levels. For example, cross-systemsequencing can be performed on a field basis as well as for data changegroups on abstract data objects. In an embodiment, the correlated dataflows between databases are determined by a machine learning examinationof data change transactions.

In step 237, analysis system 210 maintains data flow sequences throughchange frequency analysis (using statistics), system-to-system timings(again, through statistics), and comparison over time by identificationof typical versus atypical behavior, through statistics. In anembodiment, analysis system 210 will support additions of futuremiddle-systems, APIs, extract-transform-load (ETL) tools, message buses,etc. Analysis system 210 can use statistics to uncover pairwise (1:1),integrator (many:1) and distributor (1:many) data flow sequencepatterns.

Additionally, in step 238, analysis system 210 maintains statisticsaround operations, i.e., DML transactions like add/insert,change/update, remove/delete. Gathering statistics per field, datachange group or assessed data object will show the typical systembehavior. For example, if system A shows the behavior of insert, updateand delete on those fields building a postal address record, it is adata entry system. However, if system A performs these transactionsalways after system Z, system A copies the data from system Z. If, inaddition, system A typically copies data from system Z, but sometimesshows insert/update/delete type transactions on records that previouslycame from system Z, but not in this every case, system A might modifydata that likely should not be modified, or the process duration mightvary. The former case can identify ‘misbehaving’ systems or bad processdesign (unclear data ownership). Data change operation types(add/change/delete) enable identification of initial data entry (noother data flow/mapping source for data); subsequent data modification(change on data that is mapped to a source, without that source havingchanged in expected way (e.g., within typical time correlation);read-only endpoints (no change, not source for further dataflow/mapping); and read-only pass-throughs (no change, source foranother data flow/mapping). For example, the appearance of ‘PostalAddress 123’ and later modification of ‘Postal Address 123’ to ‘PostalAddress 234’ in the Ordering system would indicate a modification ofdata that originated in another system (i.e., the Sales system in theexample above, which is analyzed by the time correlated value firstappearing in the Sales system, then in the Ordering system), especiallysince a data change transaction did not also appear in an identifiedsource. Analysis system 210 stores this information in fifth database215.

Furthermore, analysis system 210 can identify the original source ofentry vs. systems that modify data content that was passed into them.This can be done using statistical analysis of event types and the MLdetermined data flow analysis described above. Analysis system 210 canactively capture system and interface changes introduced by, forexample, projects. This can be achieved by identifying metadata that waspreviously unknown, as well as data mapping and data flow additions,modifications or removals.

Results provided by the analysis system 210 will be repeatable andextensible to more systems, i.e., over a period of time, analysis system210 can grow its system reach and with that the scope of systems withinthe environment of enterprise data storage system 220. Additionally, thesystem can gather data flow latencies between systems by assessing andevaluating the event timestamps from various systems. Actual data flowlatencies based on real data flow statistics can enable business processoptimizations.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2B, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

FIGS. 2C and 2D are block diagrams illustrating an example, non-limitingembodiment of data change transactions provided to analysis system inaccordance with various aspects described herein. As shown in FIG. 2C,enterprise data storage system 220 provides exemplary data changetransactions 240, 245, 250 to analysis system 210. For example, datachange transaction 240 represents an insert transaction that addsinformation to a data table “QASOURCE.TCUSTORD” in enterprise datastorage system 220. The operational timestamp (line 3, “op_ts”)indicates the actual time that the information was committed to thedatabase, which is used by analysis system 210 to statistically analyzeand correlate data change transactions, reindexed on the values to tallylikelihoods of matches to derive data flows. Data change transaction 245represents an update transaction to change information stored in theaforementioned data table. As shown in FIG. 2D, data change transaction250 represents a delete transaction of information stored in theaforementioned data table. In an embodiment, data change transactionsare formatted using a standard format, such as Java Script ObjectNotation (JSON), for consistency. See, e.g.,docs.oracle.com/en/middleware/goldengate/big-data/19.1/gadbd/using-pluggable-formatters.html#GUID-EFBE916A-3DA3-451E-A21F-760B808BA2F0,which is incorporated by reference herein.

Referring now to FIG. 3 , a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of system 100, thesubsystems and functions of system 200, and method presented in FIGS. 1,2A, 2B, 2C, 2D and 3 . For example, virtualized communication network300 can facilitate in whole or in part gathering and sending copies ofdatabase transactions, identifying data change groups indicatingcorrelated data flows between databases by comparing data object valuesof the database transactions and determining data mappings of dataobjects stored in databases by statistical analysis of the correlateddata flows.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general-purpose processors or general-purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1 ),such as an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it iselastic: so, the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized and might require special DSP code andanalog front ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements do not typically need toforward substantial amounts of traffic, their workload can bedistributed across a number of servers—each of which adds a portion ofthe capability, and overall, which creates an elastic function withhigher availability than its former monolithic version. These virtualnetwork elements 330, 332, 334, etc. can be instantiated and managedusing an orchestration approach similar to those used in cloud computeservices.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud or might simply orchestrateworkloads supported entirely in NFV infrastructure from thesethird-party locations.

Turning now to FIG. 4 , there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part gathering and sending copies of databasetransactions, identifying data change groups indicating correlated dataflows between databases by comparing data object values of the databasetransactions and determining data mappings of data objects stored indatabases by statistical analysis of the correlated data flows.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM),flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4 , the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high-capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5 , an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part gathering and sending copies of databasetransactions, identifying data change groups indicating correlated dataflows between databases by comparing data object values of the databasetransactions and determining data mappings of data objects stored indatabases by statistical analysis of the correlated data flows. In oneor more embodiments, the mobile network platform 510 can generate andreceive signals transmitted and received by base stations or accesspoints such as base station or access point 122. Generally, mobilenetwork platform 510 can comprise components, e.g., nodes, gateways,interfaces, servers, or disparate platforms, which facilitate bothpacket-switched (PS) (e.g., internet protocol (IP), frame relay,asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic(e.g., voice and data), as well as control generation for networkedwireless telecommunication. As a non-limiting example, mobile networkplatform 510 can be included in telecommunications carrier networks andcan be considered carrier-side components as discussed elsewhere herein.Mobile network platform 510 comprises CS gateway node(s) 512 which caninterface CS traffic received from legacy networks like telephonynetwork(s) 540 (e.g., public switched telephone network (PSTN), orpublic land mobile network (PLMN)) or a signaling system #7 (SS7)network 560. CS gateway node(s) 512 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway node(s) 512 can access mobility, or roaming, data generatedthrough SS7 network 560; for instance, mobility data stored in a visitedlocation register (VLR), which can reside in memory 530. Moreover, CSgateway node(s) 512 interfaces CS-based traffic and signaling and PSgateway node(s) 518. As an example, in a 3GPP UMTS network, CS gatewaynode(s) 512 can be realized at least in part in gateway GPRS supportnode(s) (GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 512, PS gateway node(s) 518, and servingnode(s) 516, is provided and dictated by radio technology(ies) utilizedby mobile network platform 510 for telecommunication over a radio accessnetwork 520 with other devices, such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processors canexecute code instructions stored in memory 530, for example. It shouldbe appreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5 , and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6 , an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part gathering andsending copies of database transactions, identifying data change groupsindicating correlated data flows between databases by comparing dataobject values of the database transactions and determining data mappingsof data objects stored in databases by statistical analysis of thecorrelated data flows.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, Wi-Fi, DECT,or cellular communication technologies, just to mention a few(Bluetooth® and ZigBee® are trademarks registered by the Bluetooth®Special Interest Group and the ZigBee® Alliance, respectively). Cellulartechnologies can include, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS,TDMA/EDGE, EV/DO, WiMAX, SDR, LTE, as well as other next generationwireless communication technologies as they arise. The transceiver 602can also be adapted to support circuit-switched wireline accesstechnologies (such as PSTN), packet-switched wireline accesstechnologies (such as TCP/IP, VoIP, etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high-volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, Wi-Fi, Bluetooth®, or otherwireless access points by sensing techniques such as utilizing areceived signal strength indicator (RSSI) and/or signal time of arrival(TOA) or time of flight (TOF) measurements. The controller 606 canutilize computing technologies such as a microprocessor, a digitalsignal processor (DSP), programmable gate arrays, application specificintegrated circuits, and/or a video processor with associated storagememory such as Flash, ROM, RAM, SRAM, DRAM or other storage technologiesfor executing computer instructions, controlling, and processing datasupplied by the aforementioned components of the communication device600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only and doesnot otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x₁, x₂, x₃, x₄ . . .x_(n)), to a confidence that the input belongs to a class, that is, f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or.” That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to,” “coupledto,” and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device, comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: prioritizing known positivematches of data object values for data objects from past data mappingsof the data objects discovered in a plurality of data changetransactions for databases in a plurality of database systems, therebyidentifying data change groups indicating correlated data flows betweenthe databases, wherein the prioritizing comprises using a machinelearning (ML) algorithm trained on data element and metadata elementanalysis of the correlated data flows; identifying data mappings of thedata objects by a statistical analysis of the correlated data flows ofthe data change groups, wherein the correlated data flows and statisticsare determined by a ML examination of the plurality of data changetransactions; and providing results of the data mappings of the dataobjects stored in the databases.
 2. The device of claim 1, wherein thedata change transactions are provided in a consistent format.
 3. Thedevice of claim 2, wherein the consistent format comprises Java ScriptObject Notation.
 4. The device of claim 1, wherein a description of eachdata change transaction comprises one of an insert, an update or adelete.
 5. The device of claim 1, wherein the operations furthercomprise determining a cross-system data flow between the databases. 6.The device of claim 1, wherein the operations further comprise updatingthe statistical analysis after receiving additional data changetransactions.
 7. The device of claim 1, wherein the operations furthercomprise maintaining a sliding time window of data change transactions.8. The device of claim 1, wherein the results comprise data flowlatencies between the database systems.
 9. The device of claim 1,wherein the processing system comprises a plurality of processorsoperating in a distributed computing environment.
 10. A non-transitory,machine-readable medium, comprising executable instructions that, whenexecuted by a processing system including a processor, facilitateperformance of operations, the operations comprising: prioritizing knownpositive matches of data object values for data objects from past datamappings of the data objects discovered in a plurality of data changetransactions for databases in a plurality of database systems, therebyidentifying data change groups indicating correlated data flows betweenthe databases, wherein the prioritizing comprises using a machinelearning (ML) algorithm trained on data element and metadata elementanalysis of the correlated data flows; identifying data mappings of thedata objects by a statistical analysis of the correlated data flows ofthe data change groups, wherein the correlated data flows and statisticsare determined by a ML examination of the plurality of data changetransactions; and reporting results of the data mappings of the dataobjects stored in the databases.
 11. The non-transitory,machine-readable medium of claim 10, wherein the results comprise dataflow latencies between the database systems.
 12. The non-transitory,machine-readable medium of claim 10, wherein the data changetransactions are provided in a consistent format.
 13. Thenon-transitory, machine-readable medium of claim 12, wherein theconsistent format comprises Java Script Object Notation.
 14. Thenon-transitory, machine-readable medium of claim 10, wherein adescription of each data change transaction comprises one of an insert,an update or a delete.
 15. The non-transitory, machine-readable mediumof claim 10, wherein the operations further comprise determining across-system data flow between the databases.
 16. The non-transitory,machine-readable medium of claim 10, wherein the operations furthercomprise updating the statistical analysis after receiving additionaldata change transactions.
 17. The non-transitory, machine-readablemedium of claim 10, wherein the operations further comprise maintaininga sliding time window of the data change transactions.
 18. Thenon-transitory, machine-readable medium of claim 10, wherein theprocessing system comprises a plurality of processors operating in adistributed computing environment.
 19. A method, comprising:prioritizing, by a processing system including a processor, knownpositive matches of data object values for data objects from past datamappings of the data objects discovered in a plurality of data changetransactions for databases in a plurality of database systems, therebyidentifying data change groups indicating correlated data flows betweenthe databases, wherein the prioritizing comprises using a machinelearning (ML) algorithm trained on data element and metadata elementanalysis of the correlated data flows; identifying, by the processingsystem, data mappings of the data objects by a statistical analysis ofthe correlated data flows of the data change groups, wherein thecorrelated data flows and statistics are determined by a ML examinationof the plurality of data change transactions; and outputting, by theprocessing system, results of the data mappings of the data objectsstored in the databases.
 20. The method of claim 19, wherein the resultscomprise data flow latencies between the database systems.